Monica Beckwith is an Independent Consultant specializing in optimizing the Java Virtual Machine and the Garbage Collectors for enterprise applications. She is a regular speaker at various conferences and has several published articles on topics including garbage collection, the Java memory model and others. Monica led Oracle's Garbage First Garbage Collector performance team, and was named a JavaOne 2013 Rock Star.

Microsoft's online services, especially Bing, are some of most important proving grounds for running .Net in large-scale, highly available systems. The platform that underlies Bing also runs significant online functionality for Cortana, Office, Xbox, Windows and more.

When deciding how to build core infrastructure for the next version of Microsoft's query serving platform, we had to make a number of hard choices. First and...

Since Java 8, CompletableFuture has enabled asynchronous, future-based programming in Java and is one of the most powerful features suitable for creating asynchronous APIs. This presentation, based on real project experience, goes beyond the CompletableFuture public API. It reveals internal details and shows who stands to benefit from it for better performance.

Curious about Java application and JVM performance and how they are continuing to evolve? Come to this talk to learn more about exciting results and new advancements in the area of JVM performance using the latest open source JVM technology at Eclipse OpenJ9 running with OpenJDK! We'll talk about new performance boosts across a wide variety of applications and present results using different workloads and metrics to give you a fuller picture of what to expect from OpenJ9. We will also...

Instagram server is one of the biggest Python deployments in the world to support more than 700M active users. At Instagram, the computing parallelism is based on multi-processing instead of threading. Memory utilization becomes critical in such model, i.e., with less memory per process, we are able to improve the parallelism hence overall capacity. In this talk, we will start with how Python memory profiling is done at Instagram, what useful insights we got from...

Netflix runs thousands of microservices to serve more than 100M users everyday. These services are backed by large fleet of data store instances running on the public cloud. It is nearly impossible to predict the traffic patterns imposed by our architecture upon our data stores. We needed a framework that would help us determine the behavior of our platform systems under various workloads. We wanted to be mindful of provisioning our clusters, scaling them either horizontally (by adding nodes...

Learn about machine learning in practice and on the horizon. Learn about ML at Quora, Uber's Michelangelo, ML workflow with Netflix Meson and topics on Bots, Conversational interfaces, automation, and deployment practices in the space.